Index modelling for coastal ecosystem health assessment
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Coastal ecosystems are faced with a variety of environmental stresses, and the concept of ecosystem health has become increasingly attractive to both regulators and the public. Many indicators of ecosystem health have been derived, many of them data-intensive, such as those relying on benthic biodiversity. In addition to large effort, they yield information after the occurrence of impacts or other detriments to environmental quality. Prediction of impacts a priori would be more useful from a management standpoint. Although various models have predictive capability, their spin-up time may be long, requiring data that are not available for a given site. In a new research program, we therefore use system-level properties (flushing, morphology, pollutant load, etc.) to develop index-type models for coastal ecosystems. Moreover, we examine the statistical properties of their construction (error terms, non-linearity) to make them more quantitative in application. Groundtruthing of index models will be undertaken at study sites on both coasts of Canada, using data from well-known sites. In addition, a decision support tool based on combining/weighting the indices will be developed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".